Relevance vector machine and multivariate adaptive regression spline for modelling ultimate capacity of pile foundation

Document Type : Research

Authors

1 Associate Professor, Centre for Disaster Mitigation and Management, VIT University, India,

2 Associate Professor, Department of Civil Engineering, Curtin University

Abstract

This study examines the capability of the Relevance Vector Machine (RVM) and Multivariate Adaptive Regression Spline (MARS) for prediction of ultimate capacity of driven piles and drilled shafts. RVM is a sparse method for training generalized linear models, while MARS technique is basically an adaptive piece-wise regression approach. In this paper, pile capacity prediction models are developed based on data obtained from the literature and comprise in-situ pile loading tests and Cone Penetration Test (CPT) results. Equations are derived from the developed RVM and MARS models, and the prediction results are compared with those obtained from available CPT-based methods. Sensitivity has been carried out to determine the effect of each input parameter. This study confirms that the developed RVM and MARS models predict ultimate capacity of driven piles and drilled shafts reasonably well, and outperform the available methods.

Keywords

References

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Numerical Methods in Civil Engineering
Volume 1, Issue 1
September 2016
Pages 37-45

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History

  • Receive Date: 19 December 2013
  • Revise Date: 14 March 2014
  • Accept Date: 26 May 2014

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